Introduction
Load required R packages
Collect information about papers
- Pull down papers and citation info from Crossref
- Pull down info from Whedon API
- Combine with info from GitHub issues
- Add information from software repositories
- Clean up a bit
Tabulate number of missing values
Number of published papers per month and year
Citation distribution
Most cited papers
Citation count vs time since publication
Power law of citation count within each half year
Pre-review/review time over time
Languages
Association between number of citations and number of stars of the GitHub repo
Distribution of time between GitHub repo creation and JOSS submission
Distribution of time between JOSS acceptance and last commit
Number of authors per paper
Number of authors vs number of contributors to the GitHub repo
Number of reviewers per paper
Most active reviewers
Number of papers per editor and year
Distribution of software repo licenses
Save object
Session info

Introduction

In this report, we extract information about published JOSS papers and generate graphics as well as a summary table that can be downloaded and used for further analyses.

Load required R packages

suppressPackageStartupMessages({
  library(tibble)
  library(rcrossref)
  library(dplyr)
  library(tidyr)
  library(ggplot2)
  library(lubridate)
  library(gh)
  library(purrr)
  library(jsonlite)
  library(DT)
  library(plotly)
})

## Keep track of the source of each column
source_track <- c()

## Read archived version of summary data frame, to use for filling in 
## information about software repositories (due to limit on API requests)
## Sort by the date when software repo info was last obtained
papers_archive <- readRDS(gzcon(url("https://github.com/openjournals/joss-analytics/blob/gh-pages/joss_submission_analytics.rds?raw=true"))) %>%
  dplyr::arrange(!is.na(repo_info_obtained), repo_info_obtained)

Collect information about papers

Pull down papers and citation info from Crossref

We get the information about published JOSS papers from Crossref, using the rcrossref R package. This package is also used to extract citation counts.

## Fetch JOSS papers from Crossref
## Only 1000 papers at the time can be pulled down
lim <- 1000
papers <- rcrossref::cr_works(filter = c(issn = "2475-9066"), 
                              limit = lim)$data

## Warning: `tbl_df()` is deprecated as of dplyr 1.0.0.
## Please use `tibble::as_tibble()` instead.
## [90mThis warning is displayed once every 8 hours.[39m
## [90mCall `lifecycle::last_warnings()` to see where this warning was generated.[39m

i <- 1
while (nrow(papers) == i * lim) {
  papers <- dplyr::bind_rows(
    papers, 
    rcrossref::cr_works(filter = c(issn = "2475-9066"), 
                        limit = lim, offset = i * lim)$data)
  i <- i + 1
}
papers <- papers %>%
  dplyr::filter(type == "journal-article") 

## A few papers don't have DOIs - generate them from the URL
noaltid <- which(is.na(papers$alternative.id))
papers$alternative.id[noaltid] <- gsub("http://dx.doi.org/", "",
                                       papers$url[noaltid])

## Get citation info from Crossref and merge with paper details
cit <- rcrossref::cr_citation_count(doi = papers$alternative.id)
papers <- papers %>% dplyr::left_join(
  cit %>% dplyr::rename(citation_count = count), 
  by = c("alternative.id" = "doi")
)

## Remove one duplicated paper
papers <- papers %>% dplyr::filter(alternative.id != "10.21105/joss.00688")

source_track <- c(source_track, 
                  structure(rep("crossref", ncol(papers)), 
                            names = colnames(papers)))

Pull down info from Whedon API

For each published paper, we use the Whedon API to get information about pre-review and review issue numbers, corresponding software repository etc.

whedon <- list()
p <- 1
a <- jsonlite::fromJSON(
  url(paste0("https://joss.theoj.org/papers/published.json?page=", p)),
  simplifyDataFrame = FALSE
)
while (length(a) > 0) {
  whedon <- c(whedon, a)
  p <- p + 1
  a <- jsonlite::fromJSON(
    url(paste0("https://joss.theoj.org/papers/published.json?page=", p)),
    simplifyDataFrame = FALSE
  )
}

whedon <- do.call(dplyr::bind_rows, lapply(whedon, function(w) {
  data.frame(api_title = w$title, 
             api_state = w$state,
             editor = paste(w$metadata$paper$editor, collapse = ","),
             reviewers = paste(w$reviewers, collapse = ","),
             nbr_reviewers = length(w$reviewers),
             repo_url = w$repository_url,
             review_issue_id = w$review_issue_id,
             doi = w$doi,
             prereview_issue_id = ifelse(!is.null(w$meta_review_issue_id),
                                         w$meta_review_issue_id, NA_integer_),
             languages = paste(w$metadata$paper$languages, collapse = ","),
             archive_doi = w$metadata$paper$archive_doi)
}))

papers <- papers %>% dplyr::left_join(whedon, by = c("alternative.id" = "doi"))

source_track <- c(source_track, 
                  structure(rep("whedon", length(setdiff(colnames(papers),
                                                         names(source_track)))), 
                            names = setdiff(colnames(papers), names(source_track))))

Combine with info from GitHub issues

From each pre-review and review issue, we extract information about review times and assigned labels.

## Pull down info on all issues in the joss-reviews repository
issues <- gh("/repos/openjournals/joss-reviews/issues", 
             .limit = 5000, state = "all")

## From each issue, extract required information
iss <- do.call(dplyr::bind_rows, lapply(issues, function(i) {
  data.frame(title = i$title, 
             number = i$number,
             state = i$state,
             opened = i$created_at,
             closed = ifelse(!is.null(i$closed_at),
                             i$closed_at, NA_character_),
             ncomments = i$comments,
             labels = paste(setdiff(
               vapply(i$labels, getElement, 
                      name = "name", character(1L)),
               c("review", "pre-review", "query-scope", "paused")),
               collapse = ","))
}))

## Split into REVIEW, PRE-REVIEW, and other issues (the latter category 
## is discarded)
issother <- iss %>% dplyr::filter(!grepl("\\[PRE REVIEW\\]", title) & 
                                    !grepl("\\[REVIEW\\]", title))
dim(issother)

## [1] 26  7

head(issother)

##                                                       title number  state
## 1 @torressa @poulson I only found a couple of small issues:   2082 closed
## 2                         Request to regenerate final proof   2045 closed
## 3                     issues running example program Karate   2015 closed
## 4                                          @whedon commands   1898 closed
## 5                                                   @whedon   1897 closed
## 6                                    Updated issue template   1510 closed
##                 opened               closed ncomments labels
## 1 2020-02-07T09:51:50Z 2020-02-07T09:52:09Z         2       
## 2 2020-01-28T14:44:07Z 2020-01-28T14:45:26Z         2       
## 3 2020-01-15T13:25:37Z 2020-01-15T15:05:18Z         3       
## 4 2019-11-17T09:44:23Z 2019-11-17T10:26:41Z         4       
## 5 2019-11-17T09:43:49Z 2019-11-17T10:26:30Z         4       
## 6 2019-06-18T19:13:46Z 2019-06-18T19:43:30Z         0

## For REVIEW issues, generate the DOI of the paper from the issue number
getnbrzeros <- function(s) {
  paste(rep(0, 5 - nchar(s)), collapse = "")
}
issrev <- iss %>% dplyr::filter(grepl("\\[REVIEW\\]", title)) %>%
  dplyr::mutate(nbrzeros = purrr::map_chr(number, getnbrzeros)) %>%
  dplyr::mutate(alternative.id = paste0("10.21105/joss.", 
                                        nbrzeros,
                                        number)) %>%
  dplyr::select(-nbrzeros) %>% 
  dplyr::mutate(title = gsub("\\[REVIEW\\]: ", "", title)) %>%
  dplyr::rename_at(vars(-alternative.id), ~ paste0("review_", .))

## For PRE-REVIEW issues, add information about the corresponding REVIEW 
## issue number
isspre <- iss %>% dplyr::filter(grepl("\\[PRE REVIEW\\]", title)) %>%
  dplyr::filter(!grepl("withdrawn", labels)) %>%
  dplyr::filter(!grepl("rejected", labels))
## Some titles have multiple pre-review issues. In these cases, keep the latest
isspre <- isspre %>% dplyr::arrange(desc(number)) %>% 
  dplyr::filter(!duplicated(title)) %>% 
  dplyr::mutate(title = gsub("\\[PRE REVIEW\\]: ", "", title)) %>%
  dplyr::rename_all(~ paste0("prerev_", .))

papers <- papers %>% dplyr::left_join(issrev, by = "alternative.id") %>% 
  dplyr::left_join(isspre, by = c("prereview_issue_id" = "prerev_number")) %>%
  dplyr::mutate(prerev_opened = as.Date(prerev_opened),
                prerev_closed = as.Date(prerev_closed),
                review_opened = as.Date(review_opened),
                review_closed = as.Date(review_closed)) %>% 
  dplyr::mutate(days_in_pre = prerev_closed - prerev_opened,
                days_in_rev = review_closed - review_opened,
                to_review = !is.na(review_opened))

source_track <- c(source_track, 
                  structure(rep("joss-github", length(setdiff(colnames(papers),
                                                              names(source_track)))), 
                            names = setdiff(colnames(papers), names(source_track))))

Add information from software repositories

## Reorder so that software repositories that were interrogated longest 
## ago are checked first
tmporder <- order(match(papers$alternative.id, papers_archive$alternative.id),
                  na.last = FALSE)
software_urls <- papers$repo_url[tmporder]
is_github <- grepl("github", software_urls)
length(is_github)

## [1] 1015

sum(is_github)

## [1] 973

software_urls[!is_github]

##  [1] "https://bitbucket.org/cmutel/brightway2"                   
##  [2] "https://bitbucket.org/cloopsy/android/"                    
##  [3] "https://bitbucket.org/manuela_s/hcp/"                      
##  [4] "https://doi.org/10.17605/OSF.IO/3DS6A"                     
##  [5] "https://bitbucket.org/glotzer/rowan"                       
##  [6] "https://gitlab.com/moorepants/skijumpdesign"               
##  [7] "https://gitlab.com/toposens/public/ros-packages"           
##  [8] "https://gitlab.inria.fr/azais/treex"                       
##  [9] "https://bitbucket.org/basicsums/basicsums"                 
## [10] "https://savannah.nongnu.org/projects/complot/"             
## [11] "http://mutabit.com/repos.fossil/grafoscopio/"              
## [12] "https://bitbucket.org/cardosan/brightway2-temporalis"      
## [13] "https://bitbucket.org/cdegroot/wediff"                     
## [14] "https://bitbucket.org/meg/cbcbeat"                         
## [15] "https://vcs.ynic.york.ac.uk/analysis/sails"                
## [16] "https://bitbucket.org/ocellarisproject/ocellaris"          
## [17] "https://gitlab.com/QComms/cqptoolkit"                      
## [18] "https://gitlab.com/dlr-dw/ontocode"                        
## [19] "https://gitlab.com/eidheim/Simple-Web-Server"              
## [20] "https://bitbucket.org/dghoshal/frieda"                     
## [21] "https://gitlab.com/tesch1/cppduals"                        
## [22] "https://gitlab.com/gdetor/genetic_alg"                     
## [23] "https://bitbucket.org/hammurabicode/hamx"                  
## [24] "https://gitlab.com/datafold-dev/datafold/"                 
## [25] "https://bitbucket.org/likask/mofem-cephas"                 
## [26] "https://www.idpoisson.fr/fullswof/"                        
## [27] "https://sourceforge.net/p/mcapl/mcapl_code/ci/master/tree/"
## [28] "https://gricad-gitlab.univ-grenoble-alpes.fr/ttk/spam/"    
## [29] "https://c4science.ch/source/tamaas/"                       
## [30] "https://gitlab.inria.fr/miet/miet"                         
## [31] "https://bitbucket.org/mpi4py/mpi4py-fft"                   
## [32] "https://gitlab.com/myqueue/myqueue"                        
## [33] "https://gitlab.com/cerfacs/batman"                         
## [34] "https://gitlab.com/materials-modeling/wulffpack"           
## [35] "https://bitbucket.org/dolfin-adjoint/pyadjoint"            
## [36] "https://gitlab.com/costrouc/pysrim"                        
## [37] "https://bitbucket.org/rram/dvrlib/src/joss/"               
## [38] "https://ts-gitlab.iup.uni-heidelberg.de/dorie/dorie"       
## [39] "https://gitlab.com/davidtourigny/dynamic-fba"              
## [40] "https://gitlab.com/celliern/scikit-fdiff/"                 
## [41] "https://ts-gitlab.iup.uni-heidelberg.de/utopia/utopia"     
## [42] "https://ts-gitlab.iup.uni-heidelberg.de/utopia/dantro"

df <- do.call(dplyr::bind_rows, lapply(software_urls[is_github], function(u) {
  u0 <- gsub("^http://", "https://", gsub("\\.git$", "", gsub("/$", "", u)))
  if (grepl("/tree/", u0)) {
    u0 <- strsplit(u0, "/tree/")[[1]][1]
  }
  if (grepl("/blob/", u0)) {
    u0 <- strsplit(u0, "/blob/")[[1]][1]
  }
  info <- try({
    gh(gsub("(https://)?(www.)?github.com/", "/repos/", u0))
  })
  contribs <- try({
    gh(paste0(gsub("(https://)?(www.)?github.com/", "/repos/", u0), "/contributors"), 
       .limit = 500)
  })
  if (!is(info, "try-error") && length(info) > 1) {
    if (!is(contribs, "try-error")) {
      if (length(contribs) == 0) {
        repo_nbr_contribs <- repo_nbr_contribs_2ormore <- NA_integer_
      } else {
        repo_nbr_contribs <- length(contribs)
        repo_nbr_contribs_2ormore <- sum(vapply(contribs, function(x) x$contributions >= 2, NA_integer_))
        if (is.na(repo_nbr_contribs_2ormore)) {
          print(contribs)
        }
      }
    } else {
      repo_nbr_contribs <- repo_nbr_contribs_2ormore <- NA_integer_
    }
    data.frame(repo_url = u, 
               repo_created = info$created_at,
               repo_updated = info$updated_at,
               repo_pushed = info$pushed_at,
               repo_nbr_stars = info$stargazers_count,
               repo_language = ifelse(!is.null(info$language),
                                      info$language, NA_character_),
               repo_license = ifelse(!is.null(info$license),
                                     info$license$key, NA_character_),
               repo_nbr_contribs = repo_nbr_contribs,
               repo_nbr_contribs_2ormore = repo_nbr_contribs_2ormore
    )
  } else {
    NULL
  }
})) %>%
  dplyr::mutate(repo_created = as.Date(repo_created),
                repo_updated = as.Date(repo_updated),
                repo_pushed = as.Date(repo_pushed)) %>%
  dplyr::distinct() %>%
  dplyr::mutate(repo_info_obtained = lubridate::today())
stopifnot(length(unique(df$repo_url)) == length(df$repo_url))
dim(df)

## For papers not in df (i.e., for which we didn't get a valid response
## from the GitHub API query), use information from the archived data frame
dfarchive <- papers_archive %>% 
  dplyr::select(colnames(df)) %>%
  dplyr::filter(!(repo_url %in% df$repo_url))
df <- dplyr::bind_rows(df, dfarchive)

papers <- papers %>% dplyr::left_join(df, by = "repo_url")

source_track <- c(source_track, 
                  structure(rep("sw-github", length(setdiff(colnames(papers),
                                                            names(source_track)))), 
                            names = setdiff(colnames(papers), names(source_track))))

Clean up a bit

## Convert publication date to Date format
## Add information about the half year (H1, H2) of publication
## Count number of authors
papers <- papers %>% dplyr::select(-reference, -license, -link) %>%
  dplyr::mutate(published.date = as.Date(published.print)) %>% 
  dplyr::mutate(
    halfyear = paste0(year(published.date), 
                      ifelse(month(published.date) <= 6, "H1", "H2"))
  ) %>% dplyr::mutate(
    halfyear = factor(halfyear, 
                      levels = paste0(rep(sort(unique(year(published.date))), 
                                          each = 2), c("H1", "H2")))
  ) %>% dplyr::mutate(nbr_authors = vapply(author, function(a) nrow(a), NA_integer_))

source_track <- c(source_track, 
                  structure(rep("cleanup", length(setdiff(colnames(papers),
                                                          names(source_track)))), 
                            names = setdiff(colnames(papers), names(source_track))))

Tabulate number of missing values

In some cases, fetching information from (e.g.) the GitHub API fails for a subset of the publications. There are also other reasons for missing values (for example, the earliest submissions do not have an associated pre-review issue). The table below lists the number of missing values for each of the variables in the data frame.

DT::datatable(
  data.frame(variable = colnames(papers),
             nbr_missing = colSums(is.na(papers))) %>%
    dplyr::mutate(source = source_track[variable]),
  escape = FALSE, rownames = FALSE, 
  options = list(scrollX = TRUE)
)

Number of published papers per month and year

ggplot(papers %>% 
         dplyr::mutate(pubmonth = lubridate::floor_date(published.date, "month")) %>%
         dplyr::group_by(pubmonth) %>%
         dplyr::summarize(npub = n()), 
       aes(x = factor(pubmonth), y = npub)) + 
  geom_bar(stat = "identity") + theme_minimal() + 
  labs(x = "", y = "Number of published papers per month") + 
  theme(axis.title = element_text(size = 15),
        axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))

ggplot(papers %>% 
         dplyr::mutate(pubyear = lubridate::year(published.date)) %>%
         dplyr::group_by(pubyear) %>%
         dplyr::summarize(npub = n()), 
       aes(x = factor(pubyear), y = npub)) + 
  geom_bar(stat = "identity") + theme_minimal() + 
  labs(x = "", y = "Number of published papers per year") + 
  theme(axis.title = element_text(size = 15),
        axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))

Citation distribution

Papers with 20 or more citations are grouped in the “>=20” category.

ggplot(papers %>% 
         dplyr::mutate(citation_count = replace(citation_count,
                                                citation_count >= 20, ">=20")) %>%
         dplyr::mutate(citation_count = factor(citation_count, 
                                               levels = c(0:20, ">=20"))) %>%
         dplyr::group_by(citation_count) %>%
         dplyr::tally(),
       aes(x = citation_count, y = n)) + 
  geom_bar(stat = "identity") + 
  theme_minimal() + 
  labs(x = "Crossref citation count", y = "Number of publications")

Most cited papers

The table below sorts the JOSS papers in decreasing order by the number of citations in Crossref.

DT::datatable(
  papers %>% 
    dplyr::mutate(url = paste0("<a href='", url, "' target='_blank'>", 
                               url,"</a>")) %>% 
    dplyr::arrange(desc(citation_count)) %>% 
    dplyr::select(title, url, published.date, citation_count),
  escape = FALSE,
  options = list(scrollX = TRUE)
)

Citation count vs time since publication

plotly::ggplotly(
  ggplot(papers, aes(x = published.date, y = citation_count, label = title)) + 
    geom_point(alpha = 0.5) + theme_bw() + scale_y_sqrt() + 
    geom_smooth() + 
    labs(x = "Date of publication", y = "Crossref citation count") + 
    theme(axis.title = element_text(size = 15)),
  tooltip = c("label", "x", "y")
)

Power law of citation count within each half year

Here, we plot the citation count for all papers published within each half year, sorted in decreasing order.

ggplot(papers %>% dplyr::group_by(halfyear) %>% 
         dplyr::arrange(desc(citation_count)) %>%
         dplyr::mutate(idx = seq_along(citation_count)), 
       aes(x = idx, y = citation_count)) + 
  geom_point(alpha = 0.5) + 
  facet_wrap(~ halfyear, scales = "free") + 
  theme_bw() + 
  labs(x = "Index", y = "Crossref citation count")

Pre-review/review time over time

In these plots we investigate whether the time a submission spends in the pre-review or review stage has changed over time.

ggplot(papers, aes(x = prerev_opened, y = as.numeric(days_in_pre))) + 
  geom_point() + geom_smooth() + theme_bw() + 
  scale_y_sqrt() + 
  labs(x = "Date of pre-review opening", y = "Number of days in pre-review") + 
  theme(axis.title = element_text(size = 15))

ggplot(papers, aes(x = review_opened, y = as.numeric(days_in_rev))) + 
  geom_point() + geom_smooth() + theme_bw() + 
  scale_y_sqrt() + 
  labs(x = "Date of review opening", y = "Number of days in review") + 
  theme(axis.title = element_text(size = 15))

Languages

Next, we consider the languages used by the submissions. Note that a given submission can use multiple languages.

sspl <- strsplit(papers$languages, ",")
all_languages <- unique(unlist(sspl))
langs <- do.call(dplyr::bind_rows, lapply(all_languages, function(l) {
  data.frame(language = l,
             nbr_submissions = sum(vapply(sspl, function(v) l %in% v, 0)))
}))
langs %>% dplyr::arrange(desc(nbr_submissions))

##            language nbr_submissions
## 1               TeX             862
## 2            Python             576
## 3                 R             261
## 4             Shell             260
## 5          Makefile             191
## 6               C++             149
## 7              HTML             123
## 8  Jupyter Notebook             115
## 9                 C              75
## 10              CSS              66
## 11            CMake              58
## 12        Batchfile              55
## 13       JavaScript              55
## 14           Matlab              48
## 15            Julia              33
## 16          Fortran              24
## 17             Ruby              19
## 18             Java              19
## 19       PowerShell              18
## 20             Perl              11
## 21            Rebol              10
## 22               M4               7
## 23               Go               7
## 24             GLSL               7
## 25               C#               6
## 26             Roff               6
## 27                M               6
## 28             Cuda               5
## 29             Rust               5
## 30           Prolog               4
## 31              PHP               4
## 32           Smarty               4
## 33      Mathematica               4
## 34              IDL               3
## 35            Scala               3
## 36           Groovy               3
## 37            QMake               3
## 38      Objective-C               3
## 39              Awk               2
## 40             NSIS               2
## 41        Smalltalk               2
## 42         Assembly               2
## 43             Stan               2
## 44             XSLT               2
## 45         IGOR Pro               2
## 46              Vue               2
## 47            OCaml               2
## 48              Tcl               2
## 49              GAP               2
## 50              Lua               2
## 51       AGS Script               1
## 52            PLSQL               1
## 53      Common Lisp               1
## 54               eC               1
## 55            Lasso               1
## 56          Gnuplot               1
## 57            Stata               1
## 58              wdl               1
## 59           Kotlin               1
## 60       Inno Setup               1
## 61            Logos               1
## 62             Yacc               1
## 63     CoffeeScript               1
## 64               F#               1
## 65           XQuery               1
## 66                D               1
## 67             Golo               1
## 68           Scheme               1
## 69           Puppet               1
## 70            HyPhy               1
## 71       FreeMarker               1
## 72     UnrealScript               1
## 73            ANTLR               1
## 74       SourcePawn               1
## 75              Max               1
## 76              QML               1
## 77           JSONiq               1
## 78       TypeScript               1
## 79             Mako               1
## 80       Emacs Lisp               1
## 81             Hack               1

ggplot(langs %>% dplyr::arrange(desc(nbr_submissions)) %>%
         dplyr::mutate(language = factor(language, levels = language)),
       aes(x = language, y = nbr_submissions)) + 
  geom_bar(stat = "identity") + 
  theme_bw() + 
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) + 
  labs(x = "", y = "Number of submissions") + 
  theme(axis.title = element_text(size = 15))

Association between number of citations and number of stars of the GitHub repo

ggplotly(
  ggplot(papers, aes(x = citation_count, y = repo_nbr_stars,
                     label = title)) + 
    geom_point(alpha = 0.5) + scale_x_sqrt() + scale_y_sqrt() + 
    theme_bw() + 
    labs(x = "Crossref citation count", y = "Number of stars, GitHub repo") + 
    theme(axis.title = element_text(size = 15)),
  tooltip = c("label", "x", "y")
)

Distribution of time between GitHub repo creation and JOSS submission

ggplot(papers, aes(x = as.numeric(prerev_opened - repo_created))) +
  geom_histogram(bins = 50) + 
  theme_bw() + 
  labs(x = "Time (days) from repo creation to JOSS pre-review start") + 
  theme(axis.title = element_text(size = 15))

Distribution of time between JOSS acceptance and last commit

ggplot(papers, aes(x = as.numeric(repo_pushed - review_closed))) +
  geom_histogram(bins = 50) + 
  theme_bw() + 
  labs(x = "Time (days) from closure of JOSS review to most recent commit in repo") + 
  theme(axis.title = element_text(size = 15)) + 
  facet_wrap(~ year(published.date), scales = "free_y")

Number of authors per paper

List the papers with the largest number of authors, and display the distribution of the number of authors per paper, for papers with at most 20 authors.

## Papers with largest number of authors
papers %>% dplyr::arrange(desc(nbr_authors)) %>% 
  dplyr::select(title, published.date, url, nbr_authors) %>%
  as.data.frame() %>% head(10)

##                                                                                   title
## 1                                             SunPy: A Python package for Solar Physics
## 2                 ENZO: An Adaptive Mesh Refinement Code for Astrophysics (Version 2.6)
## 3                                                PyBIDS: Python tools for BIDS datasets
## 4                                     Chaste: Cancer, Heart and Soft Tissue Environment
## 5                                    spam: Software for Practical Analysis of Materials
## 6                                                 VIVO: a system for research discovery
## 7                                                              Welcome to the Tidyverse
## 8  Pyglmnet: Python implementation of elastic-net regularized generalized linear models
## 9                        HPX - The C++ Standard Library for Parallelism and Concurrency
## 10                     AMReX: a framework for block-structured adaptive mesh refinement
##    published.date                                   url nbr_authors
## 1      2020-02-14 http://dx.doi.org/10.21105/joss.01832         124
## 2      2019-10-03 http://dx.doi.org/10.21105/joss.01636          55
## 3      2019-08-12 http://dx.doi.org/10.21105/joss.01294          31
## 4      2020-03-13 http://dx.doi.org/10.21105/joss.01848          29
## 5      2020-07-13 http://dx.doi.org/10.21105/joss.02286          27
## 6      2019-07-26 http://dx.doi.org/10.21105/joss.01182          25
## 7      2019-11-21 http://dx.doi.org/10.21105/joss.01686          24
## 8      2020-03-01 http://dx.doi.org/10.21105/joss.01959          22
## 9      2020-09-02 http://dx.doi.org/10.21105/joss.02352          19
## 10     2019-05-12 http://dx.doi.org/10.21105/joss.01370          17

nbins <- max(papers$nbr_authors[papers$nbr_authors <= 20])
ggplot(papers %>% dplyr::filter(nbr_authors <= 20),
  aes(x = nbr_authors)) + 
  geom_histogram(bins = nbins, fill = "lightgrey", color = "grey50") + 
  theme_bw() + 
  facet_wrap(~ year(published.date), scales = "free_y") + 
  theme(axis.title = element_text(size = 15)) + 
  labs(x = "Number of authors", y = "Number of publications with\na given number of authors")

ggplot(papers %>% 
         dplyr::mutate(nbr_authors = replace(nbr_authors, nbr_authors > 5, ">5")) %>%
         dplyr::mutate(nbr_authors = factor(nbr_authors, levels = c("1", "2", "3", 
                                                                    "4", "5", ">5"))) %>%
         dplyr::mutate(year = year(published.date)) %>%
         dplyr::mutate(year = factor(year)) %>%
         dplyr::group_by(year, nbr_authors, .drop = FALSE) %>%
         dplyr::summarize(n = n()) %>%
         dplyr::mutate(freq = n/sum(n)) %>%
         dplyr::mutate(year = as.integer(as.character(year))), 
       aes(x = year, y = freq, fill = nbr_authors)) + geom_area() + 
  theme_minimal() + 
  scale_fill_brewer(palette = "Set1", name = "Number of\nauthors", 
                    na.value = "grey") + 
  theme(axis.title = element_text(size = 15)) + 
  labs(x = "Year", y = "Fraction of submissions")

Number of authors vs number of contributors to the GitHub repo

Note that points are slightly jittered to reduce the overlap.

plotly::ggplotly(
  ggplot(papers, aes(x = nbr_authors, y = repo_nbr_contribs_2ormore, label = title)) + 
    geom_abline(slope = 1, intercept = 0) + 
    geom_jitter(width = 0.05, height = 0.05, alpha = 0.5) + 
    # geom_point(alpha = 0.5) + 
    theme_bw() + 
    scale_x_sqrt() + scale_y_sqrt() + 
    labs(x = "Number of authors", y = "Number of contributors\nwith at least 2 commits") + 
    theme(axis.title = element_text(size = 15)),
  tooltip = c("label", "x", "y")
)

Number of reviewers per paper

Submissions associated with rOpenSci and pyOpenSci are not considered here, since they are not explicitly reviewed at JOSS.

ggplot(papers %>%
         dplyr::filter(!grepl("rOpenSci|pyOpenSci", prerev_labels)) %>%
         dplyr::mutate(year = year(published.date)),
       aes(x = nbr_reviewers)) + geom_bar() + 
  facet_wrap(~ year) + theme_bw() + 
  labs(x = "Number of reviewers", y = "Number of submissions")

Most active reviewers